Quantifying Induced Nystagmus Using a Smartphone Eye Tracking Application (EyePhone)

• Published in: Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
• Main Authors: Pouya B. Bastani, Hector Rieiro, Shervin Badihian, Jorge Otero‐Millan, Nathan Farrell, Max Parker, David Newman‐Toker, Yuxin Zhu, Ali Saber Tehrani
• Format: Article
• Language: English
• Published: Wiley 2024-01-01
• Subjects: eye movements; health technology; HINTS; nystagmus; vestibular strokes
• Online Access: https://www.ahajournals.org/doi/10.1161/JAHA.123.030927

Background There are ≈5 million annual dizziness visits to US emergency departments, of which vestibular strokes account for over 250 000. The head impulse, nystagmus, and test of skew eye examination can accurately distinguish vestibular strokes from peripheral dizziness. However, the eye‐movement signs are subtle, and lack of familiarity and difficulty with recognition of abnormal eye movements are significant barriers to widespread emergency department use. To break this barrier, we sought to assess the accuracy of EyePhone, our smartphone eye‐tracking application, for quantifying nystagmus. Methods and Results We prospectively enrolled healthy volunteers and recorded the velocity of induced nystagmus using a smartphone eye‐tracking application (EyePhone) and then compared the results with video oculography (VOG). Following a calibration protocol, the participants viewed optokinetic stimuli with incremental velocities (2–12 degrees/s) in 4 directions. We extracted slow phase velocities from EyePhone data in each direction and compared them with the corresponding slow phase velocities obtained by the VOG. Furthermore, we calculated the area under the receiver operating characteristic curve for nystagmus detection by EyePhone. We enrolled 10 volunteers (90% men) with an average age of 30.2±6 years. EyePhone‐recorded slow phase velocities highly correlated with the VOG recordings (r=0.98 for horizontal and r=0.94 for vertical). The calibration significantly increased the slope of linear regression for horizontal and vertical slow phase velocities. Evaluating the EyePhone's performance using VOG data with a 2 degrees/s threshold showed an area under the receiver operating characteristic curve of 0.87 for horizontal and vertical nystagmus detection. Conclusions We demonstrated that EyePhone could accurately detect and quantify optokinetic nystagmus, similar to the VOG goggles.